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DEPRECATED.
tf.keras.preprocessing.image.ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=1e-06,
rotation_range=0,
width_shift_range=0.0,
height_shift_range=0.0,
brightness_range=None,
shear_range=0.0,
zoom_range=0.0,
channel_shift_range=0.0,
fill_mode='nearest',
cval=0.0,
horizontal_flip=False,
vertical_flip=False,
rescale=None,
preprocessing_function=None,
data_format=None,
validation_split=0.0,
interpolation_order=1,
dtype=None
)
Used in the notebooks
Used in the guide |
---|
Methods
apply_transform
apply_transform(
x, transform_parameters
)
Applies a transformation to an image according to given parameters.
Args | |
---|---|
x
|
3D tensor, single image. |
transform_parameters
|
Dictionary with string - parameter pairs
describing the transformation.
Currently, the following parameters
from the dictionary are used:
|
Returns | |
---|---|
A transformed version of the input (same shape). |
fit
fit(
x, augment=False, rounds=1, seed=None
)
Fits the data generator to some sample data.
This computes the internal data stats related to the data-dependent transformations, based on an array of sample data.
Only required if featurewise_center
or
featurewise_std_normalization
or zca_whitening
are set to True
.
When rescale
is set to a value, rescaling is applied to
sample data before computing the internal data stats.
Args | |
---|---|
x
|
Sample data. Should have rank 4. In case of grayscale data, the channels axis should have value 1, in case of RGB data, it should have value 3, and in case of RGBA data, it should have value 4. |
augment
|
Boolean (default: False). Whether to fit on randomly augmented samples. |
rounds
|
Int (default: 1).
If using data augmentation (augment=True ),
this is how many augmentation passes over the data to use.
|
seed
|
Int (default: None). Random seed. |
flow
flow(
x,
y=None,
batch_size=32,
shuffle=True,
sample_weight=None,
seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
ignore_class_split=False,
subset=None
)
flow_from_dataframe
flow_from_dataframe(
dataframe,
directory=None,
x_col='filename',
y_col='class',
weight_col=None,
target_size=(256, 256),
color_mode='rgb',
classes=None,
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
subset=None,
interpolation='nearest',
validate_filenames=True,
**kwargs
)
flow_from_directory
flow_from_directory(
directory,
target_size=(256, 256),
color_mode='rgb',
classes=None,
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
follow_links=False,
subset=None,
interpolation='nearest',
keep_aspect_ratio=False
)
get_random_transform
get_random_transform(
img_shape, seed=None
)
Generates random parameters for a transformation.
Args | |
---|---|
img_shape
|
Tuple of integers. Shape of the image that is transformed. |
seed
|
Random seed. |
Returns | |
---|---|
A dictionary containing randomly chosen parameters describing the transformation. |
random_transform
random_transform(
x, seed=None
)
Applies a random transformation to an image.
Args | |
---|---|
x
|
3D tensor, single image. |
seed
|
Random seed. |
Returns | |
---|---|
A randomly transformed version of the input (same shape). |
standardize
standardize(
x
)
Applies the normalization configuration in-place to a batch of inputs.
x
is changed in-place since the function is mainly used internally
to standardize images and feed them to your network. If a copy of x
would be created instead it would have a significant performance cost.
If you want to apply this method without changing the input in-place
you can call the method creating a copy before:
standardize(np.copy(x))
Args | |
---|---|
x
|
Batch of inputs to be normalized. |
Returns | |
---|---|
The inputs, normalized. |